Reducing Overparameterization of Symbolic Regression Models with Equality Saturation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{de-franca:2023:GECCO,

• author = "Fabricio {Olivetti de Franca} and Gabriel Kronberger",
• title = "Reducing Overparameterization of Symbolic Regression Models with Equality Saturation",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "1064--1072",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, simplification, symbolic regression, equality saturation",
• isbn13 = "9798400701191",
• URL = "https://pure.fh-ooe.at/en/publications/reducing-overparameterization-of-symbolic-regression-models-with-",
• URL = "https://dl.acm.org/doi/abs/10.1145/3583131.3590346",
• DOI = "doi:10.1145/3583131.3590346",
• size = "9 pages",
• abstract = "Overparameterized models in regression analysis are often harder to interpret and can be harder to fit because of ill-conditioning. Genetic programming is prone to overparameterized models as it evolves the structure of the model without taking the location of parameters into account. One way to alleviate this is rewriting the expression and merging the redundant fitting parameters. In this paper we propose the use of equality saturation to alleviate overparameterization. We first notice that all the tested GP implementations suffer from overparameterization to different extents and then show that equality saturation together with a small set of rewriting rules is capable of reducing the number of fitting parameters to a minimum with a high probability. Compared to one of the few available alternatives, Sympy, it produces much better and consistent results. These results lead to different possible future investigations such as the simplification of expressions during the evolutionary process, and improvement of the interpretability of symbolic models.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Fabricio Olivetti de Franca Gabriel Kronberger

Citations